Very interested to see any code!
It should be possible to make something quite technically sweet with Julia’s ability to abstract the complexity.
Having the node information directly from the labels within a MetaGraph (i.e. this issue taken to completion https://github.com/yuehhua/GeometricFlux.jl/issues/6 ) might be a necessarily prerequisite for a smooth implementation. But equally, packing the feature vectors in your code ‘by hand’ might keep the necessary complexity exposed to the researcher / user.
A lot of the graph-based learning literature is applied to enormous graphs (e.g. social networks), so quite a different set of limits from the relatively small molecular / solid-state graphs.